






"""simple docstring"""


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers import AutoModelForCausalLM




@require_torch
class 						__UpperCamelCase     (     unittest.TestCase						):
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											lowerCAmelCase   :Optional[int]						=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
											lowerCAmelCase   :List[str]						=			AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			-1

											lowerCAmelCase   :Union[str, Any]						=			ids_tensor((1, 5)     , vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			model.generate(UpperCAmelCase     , max_new_tokens=10     , do_sample=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			tokenizer.decode(greedy_ids[0]   )

											with CaptureStdout() as cs:
																	lowerCAmelCase   :str						=			TextStreamer(UpperCAmelCase   )
																	model.generate(UpperCAmelCase     , max_new_tokens=10     , do_sample=UpperCAmelCase     , streamer=UpperCAmelCase   )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											lowerCAmelCase   :List[Any]						=			cs.out[:-1]

											self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int   )	->       List[str]:
											lowerCAmelCase   :List[str]						=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
											lowerCAmelCase   :Dict						=			AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			-1

											lowerCAmelCase   :Tuple						=			ids_tensor((1, 5)     , vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Optional[int]						=			model.generate(UpperCAmelCase     , max_new_tokens=10     , do_sample=UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			tokenizer.decode(greedy_ids[0]   )

											lowerCAmelCase   :Optional[int]						=			TextIteratorStreamer(UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											lowerCAmelCase   :Union[str, Any]						=			Thread(target=model.generate     , kwargs=UpperCAmelCase   )
											thread.start()
											lowerCAmelCase   :Any						=			''
											for new_text in streamer:
																	streamer_text += new_text

											self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Optional[int]:
											lowerCAmelCase   :Tuple						=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
											lowerCAmelCase   :List[str]						=			AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			-1

											lowerCAmelCase   :Tuple						=			ids_tensor((1, 5)     , vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Optional[Any]						=			model.generate(UpperCAmelCase     , max_new_tokens=10     , do_sample=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			greedy_ids[:, input_ids.shape[1] :]
											lowerCAmelCase   :int						=			tokenizer.decode(new_greedy_ids[0]   )

											with CaptureStdout() as cs:
																	lowerCAmelCase   :int						=			TextStreamer(UpperCAmelCase     , skip_prompt=UpperCAmelCase   )
																	model.generate(UpperCAmelCase     , max_new_tokens=10     , do_sample=UpperCAmelCase     , streamer=UpperCAmelCase   )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											lowerCAmelCase   :str						=			cs.out[:-1]

											self.assertEqual(UpperCAmelCase     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       List[str]:
											# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
											# with actual models -- the dummy models' tokenizers are not aligned with their models, and
											# `skip_special_tokens=True` has no effect on them
											lowerCAmelCase   :int						=			AutoTokenizer.from_pretrained('distilgpt2'   )
											lowerCAmelCase   :Tuple						=			AutoModelForCausalLM.from_pretrained('distilgpt2'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Any						=			-1

											lowerCAmelCase   :List[Any]						=			torch.ones((1, 5)     , device=UpperCAmelCase   ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																	lowerCAmelCase   :Optional[int]						=			TextStreamer(UpperCAmelCase     , skip_special_tokens=UpperCAmelCase   )
																	model.generate(UpperCAmelCase     , max_new_tokens=1     , do_sample=UpperCAmelCase     , streamer=UpperCAmelCase   )

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											lowerCAmelCase   :Dict						=			cs.out[:-1]  # Remove the final "\n"
											lowerCAmelCase   :Union[str, Any]						=			tokenizer(UpperCAmelCase     , return_tensors='pt'   )
											self.assertEqual(streamer_text_tokenized.input_ids.shape     , (1, 1)   )
					def        UpperCAmelCase__ (							self							:	str   )	->       int:
											lowerCAmelCase   :List[Any]						=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'   )
											lowerCAmelCase   :List[Any]						=			AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			-1

											lowerCAmelCase   :int						=			ids_tensor((1, 5)     , vocab_size=model.config.vocab_size   ).to(UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			TextIteratorStreamer(UpperCAmelCase     , timeout=0.0_0_1   )
											lowerCAmelCase   :Optional[Any]						=			{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											lowerCAmelCase   :int						=			Thread(target=model.generate     , kwargs=UpperCAmelCase   )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(UpperCAmelCase   ):
																	lowerCAmelCase   :str						=			''
																	for new_text in streamer:
																							streamer_text += new_text







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")